Search Results (1,954)

In resistance spot welding (RSW), the total electrical resistance (dynamic resistance) as the sum of bulk and contact resistance is a key variable. Both of these respective resistances influence the welding result, but the exact ratio to the total resistance of a real existing sheet is not known. Due to the high scatter in the RSW of aluminum alloys compared to steel, it is of interest to be able to explicitly determine the individual resistance components in order to gain a better understanding of the relationship between the initial state and the lower reproducibility of aluminum welding in the future. So far, only the total resistance and the bulk resistance could be determined experimentally. Due to the different sample shapes, it was not possible to consistently determine the contact resistance from the measurements. In order to realize this, a method was developed that contains the following innovations with the aid of simulation: determination of the absolute bulk resistance at room temperature (RT), determination of the absolute contact resistance at RT and determination of the ratio of bulk and contact resistance. This method makes it possible to compare the resistances of the bulk material and the surface in the initial state quantitatively. This now allows the comparison of batches regarding the surface resistance, especially for welding processes. For the aluminum sheets (EN AW-5182-O, EN AW-6014-T4) investigated, the method showed that the contact resistance dominates and the bulk resistance is less than 20%. These data can also be used to make predictions about the weldability of the alloy using artificial intelligence (AI). If experimental data are available, the method can also be applied to higher temperatures. Full article

Widely employed in diverse engineering applications, stiffened steel plates are often subjected to biaxial compressive loads. Under these conditions, buckling may occur, initially within the elastic range but potentially progressing into the elasto-plastic domain, which can lead to permanent deformations or structural collapse. To increase the ultimate buckling stress of plates, the implementation of longitudinal and transverse stiffeners is effective; however, this complexity makes analytical stress calculations challenging. As a result, numerical methods like the Finite Element Method (FEM) are attractive alternatives. In this study, the Constructal Design method and the Exhaustive Search technique were employed and associated with the FEM to optimize the geometric configuration of stiffened plates. A steel plate without stiffeners was considered, and 30% of its volume was redistributed into stiffeners, creating multiple configuration scenarios. The objective was to investigate how different arrangements and geometries of stiffeners affect the ultimate buckling stress under biaxial compressive loading. Among the configurations evaluated, the optimal design featured four longitudinal and two transverse stiffeners, with a height-to-thickness ratio of 4.80. This configuration significantly improved the performance, achieving an ultimate buckling stress 472% higher than the unstiffened reference plate. In contrast, the worst stiffened configuration led to a 57% reduction in performance, showing that not all stiffening strategies are beneficial. These results demonstrate that geometric optimization of stiffeners can significantly enhance the structural performance of steel plates under biaxial compression, even without increasing material usage. The approach also revealed that intermediate slenderness values lead to better stress distribution and delayed local buckling. Therefore, the methodology adopted in this work provides a practical and effective tool for the design of more efficient stiffened plates. Full article

The gyroid minimal surface is one type of triply periodic minimal surface (TPMS). TPMS is a minimal surface replicated in the three main directions of the Cartesian coordinate system. The minimal surface is a surface stretched between two objects, known as the smallest possible area (e.g., a soap bubble with a saddle shape stretched between two parallel circles). The complicated shape of the TPMS makes its production possible only by additive methods (3D printing). This article presents the results of experimental studies on heat transfer and flow resistance in a heat exchanger made of stainless steel. The heat exchange surface, a TPMS gyroid, separates two working media: hot and cold water. The water flow rate was varied in the range from 8 kg/h to 25 kg/h (Re = 246–1171). The water temperature at the inlet to the exchanger was maintained at a constant level of 8.8 ± 0.3 °C and 49.5 ± 0.5 °C for cold and hot water, respectively. The effect of water flow rate on the change in its temperature, the heat output of the exchanger, the average heat transfer coefficient, pressure drop, and overall resistance factor was presented. Full article

430 stainless steel exhibits soft magnetic properties, excellent formability, and corrosion resistance, making it widely used in industrial applications. This study investigates the effects of different cold working rates on the properties of 430 stainless steel subjected to various magnetic annealing atmospheres (F-1.5Si, F-1.5Si-10%, F-1.5Si-40%, F-1.5Si-10% (MA), F-1.5Si-40% (MA), F-1.5Si-10% (H₂), and F-1.5Si-40% (H₂)). The results indicate that increasing the cold working rate improves the material’s mechanical properties; however, it negatively impacts its magnetic and corrosion resistance properties. Additionally, the magnetic annealing process improves the mechanical properties, while atmospheric magnetic annealing optimizes the overall magnetic performance. In contrast, magnetic annealing in a hydrogen atmosphere does not enhance the magnetic properties as effectively as atmospheric magnetic annealing. Still, it promotes the formation of a protective layer, preserving the mechanical properties and providing better corrosion resistance. Furthermore, regardless of whether magnetic annealing is conducted in an atmospheric or hydrogen environment, materials with 10% cold work rate (F-1.5Si-10% (MA) and F-1.5Si-10% (H₂)) exhibit the lowest coercive force (286 and 293 A/m in the 10 Hz test condition), making them ideal for electromagnetic applications. Full article

This paper reports a statistical analysis of a database archiving information on the strengths of the materials in existing Italian bridges having pre- and post-tensioned concrete beams. Data were collected in anonymous form by analyzing a stock of about 170 bridges built between 1960 and 2000 and located in several Italian regions. To date, the database refers to steel reinforcing bars, concrete, and prestressing steel, whose strengths were gathered from design nominal values, acceptance certificates, and in situ test results, all derived by consulting the available documents for each examined bridge. At first, this paper describes how the available data were collected. Then, the results of a statistical analysis are presented and commented on. Moreover, goodness-of-fit tests are carried out to verify the assumption validity of a normal distribution for steel reinforcing bars and prestressing steel, and a log-normal distribution for concrete. The database represents a valuable resource for researchers and practitioners for the assessment of existing bridges. It may be applied for the use of prior knowledge within a framework where Bayesian methods are included for reducing uncertainties. The database provides essential information on the strengths of the materials to be used for a simulated design and/or for verification in the case of limited knowledge. Goodness-of-fit tests make the collected information very useful, even if probabilistic methods are applied. Full article

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

The 1.9 billion metric tons of steel globally manufactured in 2023 justify the steel industry’s pivotal role in modern society’s growth. Considering the rapid development of countries that have not fully taken part in the global market, such as Africa, steel production is expected to increase in the next decade. However, the environmental burden associated with steel manufacturing must be mitigated to achieve sustainable production, which would align with the European Green Deal pathway. Such a burden is associated both with the GHG emissions and with the solid residues arising from steel manufacturing, considering both the integrated and electrical routes. The valorisation of the main steel residues from the electrical steelmaking is the central theme of this work, referring to the steel electric manufacturing in the Dalmine case study. The investigation was carried out from two different points of view, comprising the action of a plasma electric reactor and a RecoDust unit to optimize the recovery of iron and zinc, respectively, being the two main technologies envisioned in the EU-funded research project ReMFra. This work focuses on those preliminary steps required to detect the optimal recipes to consider for such industrial units, such as thermodynamic modelling, testing the mechanical properties of the briquettes produced, and the smelting trials carried out at pilot scale. However, tests for the usability of the dusty feedstock for RecoDust are carried out, and, with the results, some recommendations for pretreatment can be made. The outcomes show the high potential of these streams for metal and mineral recovery. Full article

The production of iron and steel plays a significant role in the anthropogenic carbon footprint, accounting for 7% of global GHG emissions. In the context of CO₂ mitigation, the steelmaking industry is looking to potentially replace traditional carbon-based ironmaking processes with hydrogen-based direct reduction of iron ore in shaft furnaces. Before industrialization, detailed modeling and parametric studies were needed to determine the proper operating parameters of this promising technology. The modeling approach selected here was to complement REDUCTOR, a detailed finite-volume model of the shaft furnace, which can simulate the gas and solid flows, heat transfers and reaction kinetics throughout the reactor, with an extension that describes the whole gas circuit of the direct reduction plant, including the top gas recycling set up and the fresh hydrogen production. Innovative strategies (such as the redirection of part of the bustle gas to a cooling inlet, the use of high nitrogen content in the gas, and the introduction of a hot solid burden) were investigated, and their effects on furnace operation (gas utilization degree and total energy consumption) were studied with a constant metallization target of 94%. It has also been demonstrated that complete metallization can be achieved at little expense. These strategies can improve the thermochemical state of the furnace and lead to different energy requirements. Full article

A novel acid-free oxygen pressure leaching for the extraction of zinc from hemimorphite was proposed in this study. Green vitriol (FeSO₄·7H₂O), as one of the important industrial by-products, was used as the leaching reagent to separate zinc from silicon and iron. The effect of leaching conditions, including Fe/Zn molar ratio, leaching temperature, pressure, and reaction time, on the leaching efficiency of zinc, Fe, and Si was investigated systematically. The results showed that the molar ratio of Fe/Zn and leaching temperature play a pivotal role in determining the leaching efficiency rate of Zn. Under the optimized leaching conditions (Fe/Zn molar ratio = 6:1, 150 °C, 1.8 × 10⁶ Pa, and leaching time of 2 h), the leaching efficiency of Zn reached 98.80% and the leaching efficiencies of Fe and Si were 0.76% and 16.80%, respectively. In addition, the shrinking core model was established to represent the relationship between the rate control step and the leaching conditions. The leaching process was controlled by chemical reaction and diffusion, and the activation energy of the leaching process is 97.14 kJ/mol. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Chloride ion penetration is one of the most aggressive threats to reinforced concrete, as it triggers the electrochemical corrosion of steel reinforcement, compromising structural integrity and durability. Chloride ingress occurs through the porous structure of concrete, making permeability control crucial for enhancing structural longevity. Fiber-reinforced concrete (FRC) is widely used to improve durability; however, the effects of different fiber types on chloride resistance remain unclear. This study examines the influence of glass and polypropylene fibers on concrete’s microstructure and chloride penetration resistance. Cylindrical specimens were prepared, including a reference mix without fibers and mixes with 0.25% and 0.50% fiber content by volume. Both fiber types were tested for chloride resistance. The accelerated non-steady-state migration method was employed to determine the resistance coefficients to chloride ion penetration, while X-ray macrofluorescence (MacroXRF) mapped the chlorine infiltration depth in the samples. Compressive strength decreased in all fiber-reinforced samples, with 0.50% glass fiber leading to a 56% reduction in strength. Nevertheless, the XRF results showed that a 0.25% fiber content significantly reduced chloride penetration, with polypropylene fibers outperforming glass fibers. These findings highlight the critical role of fiber type and volume in improving concrete durability, offering insights for designing long-lasting FRC structures in chloride-rich environments. Full article

The electrooxidation (EO) of three important environmental contaminants, anticorrosive 1H-benzotriazole (BTA), plasticizer dibutyl phthalate (DBP), and non-ionic surfactant Triton X-100 (tert-octylphenoxy[poly(ethoxy)] ethanol, t-OPPE), was studied as a possible means to improve their elimination from wastewaters, which are an important emission source. EO was performed in a batch reactor with a boron-doped diamond (BDD) anode and a stainless steel cathode. Different supporting electrolytes were tested: NaCl, H₂SO₄, and Na₂SO₄. Results were analysed from the point of their efficacy in terms of degradation rate, kinetics, energy consumption, and transformation products. The highest degradation rate, shortest half-life, and lowest energy consumption was observed in the electrolyte H₂SO₄, followed by Na₂SO₄ with only slightly less favourable characteristics. In both cases, degradation was probably due to the formation of persulphate or sulphate radicals. Transformation products (TPs) were studied mainly in the sulphate media and several oxidation products were identified with all three contaminants, while some evidence of progressive degradation, e.g., ring-opening products, was observed only with t-OPPE. The possible reasons for the lack of further degradation in BTA and DBP are too short of an EO treatment time and perhaps a lack of detection due to unsuitable analytical methods for more polar TPs. Results demonstrate that BDD-based EO is a robust method for the efficient removal of structurally diverse organic contaminants, making it a promising candidate for advanced water treatment technologies. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article

Early evacuation during bushfires remains the safest strategy; however, in many realistic scenarios, timely evacuation is challenging, making safe sheltering a last-resort option to reduce risk compared to late evacuation attempts. However, most Australian homes in bushfire-prone areas are neither designed nor retrofitted to provide adequate protection against extreme bushfires, raising safety concerns. This study addresses this gap by investigating the concept of retrofitting a part of the residential buildings as attached safe rooms for sheltering and protection of valuables, providing a potential last-resort solution for bushfire-prone communities. Numerical simulations were conducted using the Fire Dynamics Simulator to assess heat transfer and internal temperature conditions in a representative residential building under bushfire exposure conditions. The study investigated the impact of the placement of the safe room relative to the fire front direction, failure of vulnerable building components, and the effectiveness of steel shutters in response to internal temperatures. The results showed that the strategic placement of safe rooms inside the building, along with adequate protective measures for windows, can substantially reduce internal temperatures. The findings emphasised the importance of maintaining the integrity of openings and the external building envelope, demonstrating the potential of retrofitted attached safe rooms as a last-resort solution for existing residential buildings in bushfire-prone areas where the entire building was not constructed to withstand bushfire conditions. Full article

Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments. Full article